On-Call Maintenance Specialist, Data Science

JobgetherData science education

Based in IndiaContractJunior

Salary not disclosed

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Job Details

Experience: At least 2 years of professional experience in a data-related role
Required Skills: AWSPythonSQLMicrosoft Power BINumpyTableauAzurePandasData visualizationscikit-learn

Have at least 2 years of professional experience in a data-related role such as Data Analyst, Data Scientist, Data Engineer, or BI Developer.
Have strong knowledge of data wrangling, exploratory data analysis, basic statistics, and data interpretation.
Demonstrate strong SQL skills, including writing, debugging, and optimizing queries.
Have hands-on Python data-work experience, including pandas, NumPy, scikit-learn, and data visualization libraries.
Have experience creating or interpreting visualizations and dashboards, with familiarity with Power BI or Tableau.
Be familiar with foundational AWS or Azure data services and architectures, including storage, compute, data warehouses, and data lake patterns.
Have working knowledge of Docker, Jupyter notebooks, and notebook-based data workflows.
Have experience using version control systems such as Git or GitHub.
Be able to debug and update Python- and SQL-based exercises, projects, and technical environments.
Be able to troubleshoot package conflicts, environment mismatches, SQL errors, visualization issues, and other technical problems.
Be able to document technical changes and create clear, step-by-step instructions.
Be able to work independently on assigned maintenance projects and collaborate with cross-functional teams.

Analyze course performance data and learner feedback to identify content that needs maintenance or improvement.
Prioritize actionable technical and instructional updates based on student feedback.
Troubleshoot issues across Python, SQL, R, notebooks, visualizations, exercises, projects, and technical documentation.
Update course instructions, examples, screenshots, diagrams, queries, expected outputs, and other materials to reflect current tools and practices.
Refresh exercises and projects using modern data workflows, APIs, libraries, and technologies such as pandas, scikit-learn, and PySpark.
Improve project rubrics, starter code, datasets, and learning materials for clarity, reliability, and robustness.
Validate browser-based Jupyter, SQL, and VS Code learning environments, including packages, programming environments, and tool compatibility.
Test and troubleshoot AWS and Azure learning environments, including services, permissions, storage, databases, compute, and streaming infrastructure.
Investigate access and configuration issues involving federated cloud accounts, IAM/RBAC permissions, data access, and resource usage.
Document changes and provide step-by-step technical guidance.

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